Add LiveVisualize tutorial

tutorials/CMakeLists.txt

@@ -753,6 +753,7 @@ if(ROOT_pyroot_FOUND)
   endif()
   if(NOT test_distrdf_dask)
     list(APPEND pyveto dataframe/distrdf002_dask_connection.py)
+    list(APPEND pyveto dataframe/distrdf003_live_visualization.py)
   endif()
   # Use main Python executable to run in PySpark driver and executors
   if(test_distrdf_pyspark)

tutorials/dataframe/distrdf003_live_visualization.py

@@ -0,0 +1,73 @@
+## \file
+## \ingroup tutorial_dataframe
+## \notebook -draw
+## Configure a Dask connection and visualize the filling of a 1D and 2D
+## histograms distributedly.
+##
+## This tutorial showcases the process of setting up real-time data representation 
+## for distributed computations.
+## By calling the LiveVisualize function, you can observe the canvas updating
+## with the intermediate results of the histograms as the
+## distributed computation progresses. 
+##
+## \macro_code
+## \macro_image
+##
+## \date August 2023
+## \author Silia Taider
+from dask.distributed import LocalCluster, Client
+import ROOT
+
+# Import the live visualization function
+LiveVisualize = ROOT.RDF.Experimental.Distributed.LiveVisualize
+
+# Point RDataFrame calls to Dask RDataFrame object
+RDataFrame = ROOT.RDF.Experimental.Distributed.Dask.RDataFrame
+
+# Function to create a Dask cluster and return the client
+def create_connection():
+    cluster = LocalCluster(n_workers=4, threads_per_worker=1, processes=True, memory_limit="2GiB")
+    client = Client(cluster)
+    return client
+
+# Function to fit a Gaussian function to the plot
+def fit_gaus(plot):
+    plot.Fit("gaus")
+
+if __name__ == "__main__":
+    # Setup connection to a Dask cluster
+    connection = create_connection()
+
+    # Create an RDataFrame that will use Dask as a backend for computations
+    num_entries = 100000000
+    d = RDataFrame(num_entries, daskclient=connection, npartitions=30)
+
+    # Define a gaussean distribution with a variable mean
+    dd = d.Define("x", f"gRandom->Gaus(10*rdfentry_/{num_entries}, 2)")\
+          .Define("y", f"gRandom->Gaus(10*rdfentry_/{num_entries}, 3)")\
+
+    # Create a 1D and a 2D histogram using the defined columns
+    h_normal_1d = dd.Histo1D(("normal_1d", "1D Histogram of a Normal Distribution",
+                              100, -10, 20),
+                              "x")
+
+    h_normal_2d = dd.Histo2D(("normal_2d", "2D Histogram of a Normal Distribution",
+                              100, -15, 25, 
+                              100, -15, 25
+                              ), "x", "y")
+
+    # Apply LiveVisualize to the histograms. 
+    # The `fit_gaus` function will be applied to the accumulating partial result 
+    # of the 1D histogram. The 2D histogram will not be further modified, just drawn. 
+    # Find more details about usage of LiveVisualize in the RDataFrame docs.
+    LiveVisualize({h_normal_1d: fit_gaus, h_normal_2d: None})
+
+    # Plot the histograms side by side on a canvas
+    c = ROOT.TCanvas("distrdf003", "distrdf003", 1600, 400)
+    c.Divide(2, 1)
+    c.cd(1)
+    h_normal_1d.Draw()
+    c.cd(2)
+    h_normal_2d.Draw()
+
+    c.Update()